Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images
National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Cardiff University（卡迪夫大学）；Newcastle Research and Innovation Institute（纽卡斯尔研究创新研究所）；National University Hospital（国立大学医院）
Tan Hung Pham, Sripad Krishna Devalla, Aloysius Ang, Soh Zhi Da, Alexandre H. Thiery, Craig Boote, Ching-Yu Cheng, Victor Koh, Michael J. A. Girard
Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle closure glaucoma. In this study, we developed a deep convolutional neural network (DCNN) for the localization of the scleral spur, and the segmentation of anterior segment structures (iris, corneo-sclera shell, anterior chamber). With limited training data, the DCNN was able to detect the scleral spur on unseen ASOCT images as accurately as an experienced ophthalmologist; and simultaneously isolated the anterior segment structures with a Dice coefficient of 95.7%. We then automatically extracted eight clinically relevant ASOCT parameters and proposed an automated quality check process that asserts the reliability of these parameters. When combined with an OCT machine capable of imaging multiple radial sections, the algorithms can provide a more complete objective assessment. This is an essential step toward providing a robust automated framework for reliable quantification of ASOCT scans, for applications in the diagnosis and management of angle closure glaucoma.
Inception Institute of Artificial Intelligence（起源人工智能研究院）；State Key Laboratory, Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心国家重点实验室）；Baidu（百度）；South China University of Technology（华南理工大学）；National Scientific and Technical Research Council, CONICET（阿根廷国家科学与技术研究理事会）；Yatiris Group, PLADEMA Institute, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN)（布宜诺斯艾利斯省中央国立大学）
Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually lead to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In p…
Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Guangzhou Shiyuan Electronic Technology Company Limited（广州视源电子科技股份有限公司）；Microsoft Research（微软研究院）；Singapore Eye Research Institute（新加坡眼科研究院）；Cixi Institute of Biomedical Engineering, CAS（中国科学院慈溪生物医学工程研究所）
A major cause of irreversible visual impairment is angle-closure glaucoma, which can be screened through imagery from Anterior Segment Optical Coherence Tomography (AS-OCT). Previous computational diagnostic techniques address this screening problem by extracting specific clinical measurements or handcrafted visual features from the images for classification. In this paper, we instead propose to learn from training data a discriminative representation that may capture subtle visual cues not modeled by predefined features. Based on clinical priors, we formulate this learning with a presented Mu…
Inception Institute of Artificial Intelligence（人工智能初创研究院）；Institute for Infocomm Research, Agency for Science, Technology and Research（科学、技术和研究局信息通信研究院）；Baidu Inc.（百度公司）；Microsoft Research（微软研究院）；Singapore Eye Research Institute（新加坡眼科研究院）；Nanyang Technological University（南洋理工大学）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；Southern University of Science and Technology（南方科技大学）
Irreversible visual impairment is often caused by primary angle-closure glaucoma, which could be detected via Anterior Segment Optical Coherence Tomography (AS-OCT). In this paper, an automated system based on deep learning is presented for angle-closure detection in AS-OCT images. Our system learns a discriminative representation from training data that captures subtle visual cues not modeled by handcrafted features. A Multi-Level Deep Network (MLDN) is proposed to formulate this learning, which utilizes three particular AS-OCT regions based on clinical priors: the global anterior segment str…
Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net
IIT Bhubaneswar（印度理工学院布巴内斯瓦尔分校）；Aravind Eye Care Systems（阿拉文德眼科医疗系统）；Singapore Eye Research Institute（新加坡眼科研究所）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）
本研究提出基于 Residual U-Net 架构的增强去噪模型，有效降低 OCT 图像中的散斑噪声并保留组织细节，在 ASOCT 和 PSOCT 两种模态上均显著提升了图像质量指标，助力更精确的临床诊断。
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；Baidu Inc（百度公司）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心）；Southern University of Science and Technology（南方科技大学）；Inception Institute of Artificial Intelligence（启科人工智能研究院）
Anterior chamber angle (ACA) classification is a key step in the diagnosis of angle-closure glaucoma in Anterior Segment Optical Coherence Tomography (AS-OCT). Existing automated analysis methods focus on a binary classification system (i.e., open angle or angle-closure) in a 2D AS-OCT slice. However, clinical diagnosis requires a more discriminating ACA three-class system (i.e., open, narrow, or synechiae angles) for the benefit of clinicians who seek better to understand the progression of the spectrum of angle-closure glaucoma types. To address this, we propose a novel sequence multi-scale …
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院高级医学计算与分析中心）；Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院戈登医学影像中心）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院放射科）
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network. Methods: In this retrospective study, we evaluated 1,216 patients from two separate institutions who underwent CT for the presence of saccular IA>=2.5 mm. A two-step model was implemented: a 3D region proposal network for initial aneurysm detection and 3D DenseNetsfor false-pos…
American University of Beirut（贝鲁特美国大学）；Reinstein Vision；London Vision Clinic（伦敦视力诊所）；Columbia University Medical Center（哥伦比亚大学医学中心）；Sorbonne Université（索邦大学）；Biomedical Science Research Institute, Ulster University（阿尔斯特大学生物医学科学研究所）；Bascom Palmer Eye Institute, University of Miami Miller School of Medicine（迈阿密大学米勒医学院巴斯康帕尔默眼科研究所）；Stanford University（斯坦福大学）；Saint Joseph University（圣约瑟夫大学）；American University of Beirut Medical Center（贝鲁特美国大学医学中心）
ARCOS: Zero-shot Boundary Localization for Corneal Layer Segmentation Across Optical Coherence Tomography Devices
Laboratoire d’Optique et Biosciences, CNRS, INSERM, École polytechnique, Institut Polytechnique de Paris（光学与生物光学实验室，法国国家科学研究中心，法国健康与医学研究院，巴黎综合理工学院，巴黎理工学院）；GRC 32, Transplantation et Thérapies Innovantes de la Cornée, Sorbonne Université, Centre Hospitalier National d’Ophtalmologie des Quinze-Vingts（GRC 32，角膜创新移植与治疗研究组，索邦大学，十五二十国家眼科中心）
Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）
Optical Coherence Tomography (OCT) is an imaging modality that has been widely adopted for visualizing corneal, retinal and limbal tissue structure with micron resolution. It can be used to diagnose pathological conditions of the eye, and for developing pre-operative surgical plans. In contrast to the posterior retina, imaging the anterior tissue structures, such as the limbus and cornea, results in B-scans that exhibit increased speckle noise patterns and imaging artifacts. These artifacts, such as shadowing and specularity, pose a challenge during the analysis of the acquired volumes as they…
Primary angle-closure disease (PACD) is a severe retinal disease, which might cause irreversible vision loss. In clinic, accurate identification of angle-closure and localization of the scleral spur's position on anterior segment optical coherence tomography (AS-OCT) is essential for the diagnosis of PACD. However, manual delineation might confine in low accuracy and low efficiency. In this paper, we propose an efficient and accurate end-to-end architecture for angle-closure classification and scleral spur localization. Specifically, we utilize a revised ResNet152 as our backbone to improve th…
Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices
Division AI4Health, Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学AI4Health部门）；University Institute for Diagnostic and Interventional Radiology, Klinikum Oldenburg AöR（奥尔登堡医院大学诊断与介入放射学研究所）
Harbin Engineering University（哈尔滨工程大学）；Monash University（莫纳什大学）；Beijing Airdoc Technology Co., Ltd.（北京鹰瞳科技发展股份有限公司）；Chongqing Hospital of Traditional Chinese Medicine（重庆市中医院）；Chongqing Renji Hospital of Chinese Academy of Sciences（中国科学院重庆仁济医院）
Usually, lesions are not isolated but are associated with the surrounding tissues. For example, the growth of a tumour can depend on or infiltrate into the surrounding tissues. Due to the pathological nature of the lesions, it is challenging to distinguish their boundaries in medical imaging. However, these uncertain regions may contain diagnostic information. Therefore, the simple binarization of lesions by traditional binary segmentation can result in the loss of diagnostic information. In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i.e., a soft mask…
Tencent Jarvis Lab, Tencent（腾讯杰维斯实验室，腾讯）；National University of Defense Technology（国防科技大学）
Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people. Early detection of PACG is essential, so as to provide timely treatment and minimize the vision loss. In the clinical practice, PACG is diagnosed by analyzing the angle between the cornea and iris with anterior segment optical coherence tomography (AS-OCT). The rapid development of deep learning technologies provides the feasibility of building a computer-aided system for the fast and accurate segmentation of cornea and iris tissues. However, the application of deep learning methods in the …
University of Tabriz（大不里士大学）；University of Tehran（德黑兰大学）；Tehran University of Medical Sciences（德黑兰医科大学）
Background and Objective: The Acetabular version, an essential factor in total hip arthroplasty, is measured by CT scan as the gold standard. The dose of radiation and expensiveness of CT make anterior-posterior pelvic radiograph an appropriate alternative procedure. In this study, we applied a deep learning approach on anteroposterior pelvic X-rays to measure anatomical version, eliminating the necessity of using Computed tomography scan. Methods: The right and left acetabular version angles of the hips of 300 patients are computed using their CT images. The proposed deep learning model, Atte…
Background: Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning models that yield computer-aided solutions widely usable in clinical practice. However, most studies are conducted with small samples and the models are used as black boxes. Clinicians should not trust machine learning decisions unless they come with comprehensive and easily unde…
Ege University（埃杰大学）；University of Central Florida（中佛罗里达大学）；University of Pennsylvania（宾夕法尼亚大学）
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machinelearning, specifically deep learning methods, can be used for rapidand accurate image analysis of MRI scans, an unmet clinicalneed in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and stu…
Cross Modality Image Translation In Medical Imaging Using Generative Frameworks
Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与介入部门、放射物理、生物医学工程，乌梅大学）；Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma（人工智能与计算机系统单位，工程部门，罗马生物医学学院）；Vita-Salute San Raffaele University（维塔-萨拉特·桑拉法埃莱大学）；Department of Medicine, Surgery and Dentistry, University of Salerno（医学、外科和牙科部门，萨勒诺大学）；Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel（诊断和介入神经放射学部门，放射学部门，巴塞尔大学医院）；Department of Pediatric Radiology, University Children’s Hospital Basel（儿科放射学部门，巴塞尔儿童医院）；Department of Life Science and Public Health, Università Cattolica del Sacro Cuore（生命科学与公共健康部门，圣心大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿提诺拉A·马里诺斯生物医学成像中心）；Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete（人工智能与转化成像（ATI）实验室，放射学部门，医学院，克里特大学）；Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute（放射学部门，临床科学、介入和科技（CLINTEC）部门，卡罗林斯卡研究所）；Columbia University Medical Center（哥伦比亚大学医学中心）；Department of Diagnostics and intervention, Diagnostic radiology, Umeå University（诊断与介入部门，诊断放射学，乌梅大学）
We present a plenoptic microscopy configuration for 3D snapshot imaging, which is dual telecentric and can directly record true projection images corresponding with different viewing angles. It also allows blocking high-angle stray rays without sacrificing the light collection efficiency. This configuration named as snapshot projection optical tomography (SPOT) arranges an objective lens and a microlens array (MLA) in a 4-f telecentric configuration and places an aperture stop at the back focal plane of a relay lens. We develop a forward imaging model for SPOT, which can also be applied to exi…
CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation
Université Sorbonne Paris Nord（索邦巴黎北大学）；University of Dubai（迪拜大学）；Northwestern University（西北大学）；IULM University（IULM大学）；Norwegian University of Science and Technology（挪威科学与技术大学）；University of Geneva（日内瓦大学）
Eindhoven University of Technology（埃因霍温理工大学）；Rigshospitalet - Glostrup（格洛斯楚普国立医院）；Copenhagen Eye Foundation（哥本哈根眼科基金会）
Purpose: We developed a method to automatically locate and quantify graft detachment after Descemet's Membrane Endothelial Keratoplasty (DMEK) in Anterior Segment Optical Coherence Tomography (AS-OCT) scans. Methods: 1280 AS-OCT B-scans were annotated by a DMEK expert. Using the annotations, a deep learning pipeline was developed to localize scleral spur, center the AS-OCT B-scans and segment the detached graft sections. Detachment segmentation model performance was evaluated per B-scan by comparing (1) length of detachment and (2) horizontal projection of the detached sections with the expert…
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学研究生医学院）；Institute for Molecular and Clinical Ophthalmology（分子与临床眼科研究所）；Rigshospitalet（丹麦国家医院）；University of Copenhagen（哥本哈根大学）；Yong Loo Lin School of Medicine（杨潞龄医学院）；National University of Singapore（新加坡国立大学）；Save Sight Institute（拯救视力研究所）；The University of Sydney（悉尼大学）
Purpose: (1) To develop a deep learning algorithm to identify major tissue structures of the optic nerve head (ONH) in 3D optical coherence tomography (OCT) scans; (2) to exploit such information to robustly differentiate among healthy, optic disc drusen (ODD), and papilledema ONHs. It was a cross-sectional comparative study with confirmed ODD (105 eyes), papilledema due to high intracranial pressure (51 eyes), and healthy controls (100 eyes). 3D scans of the ONHs were acquired using OCT, then processed to improve deep-tissue visibility. At first, a deep learning algorithm was developed using …
